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    "# Scattering Cross Section of a Finite Dielectric Cylinder\n",
    "\n",
    "As an alternative to the \"ring\" sources of the previous examples, it is also possible to launch planewaves in cylindrical coordinates. This is demonstrated in this example which involves computing the scattering cross section of a finite-height dielectric cylinder. The results for the 2d simulation involving the cylindrical $(r,z)$ cell are validated by comparing to the same simulation in 3d Cartesian coordinates which tends to be much slower and less accurate at the same grid resolution.\n",
    "\n",
    "The calculation of the scattering cross section is described in [Tutorial/Basics/Mie Scattering of a Lossless Dielectric Sphere](https://meep.readthedocs.io/en/latest/Python_Tutorials/Basics/#mie-scattering-of-a-lossless-dielectric-sphere) which is modified for this example. A linearly-polarized planewave is normally incident on a $z$-oriented cylinder which is enclosed by a DFT flux box. Expressed in cylindrical coordinates, an $x$-polarized planewave propagating in the $z$ direction is the sum of two circularly-polarized planewaves of opposite chirality:\n",
    "\n",
    "<center>\n",
    "$$ \\hat{E}_x = \\frac{1}{2} \\left[e^{i\\phi}(\\hat{E}_\\rho + i\\hat{E}_\\phi) + e^{-i\\phi}(\\hat{E}_\\rho - i\\hat{E}_\\phi)\\right] $$\n",
    "</center>\n",
    "\n",
    "In practice, this involves performing two separate simulations for $m=\\pm 1$. The scattered power from each simulation is then simply summed since the cross term in the total Poynting flux cancels for the different $m$ values when integrated over the $\\phi$ direction. However, only one of the two simulations is necessary: the scattered power is the same for $m=\\pm 1$ due to the mirror symmetry of the structure. (Note that a linearly-polarized planewave is *not* $m=0$, which corresponds to a field pattern that is *invariant* under rotations similar to [TE<sub>01</sub>/TM<sub>01</sub> modes](https://en.wikipedia.org/wiki/Transverse_mode). A linear polarization is the superposition of left and right circularly-polarized waves ($m=\\pm 1$) and is *not* rotationally invariant; it flips sign if it is rotated by 180°.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "time for choose_chunkdivision = 0.000299931 s\n",
      "Working in Cylindrical dimensions.\n",
      "Computational cell is 4 x 0 x 8.88 with resolution 25\n",
      "time for set_epsilon = 0.052002 s\n",
      "-----------\n",
      "Meep: using complex fields.\n"
     ]
    },
    {
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      "text/plain": [
       "FloatProgress(value=0.0, description='0% done ', max=15.49778699874878)"
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     "output_type": "stream",
     "text": [
      "run 0 finished at t = 15.5 (775 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "time for choose_chunkdivision = 0.000163078 s\n",
      "Working in Cylindrical dimensions.\n",
      "Computational cell is 4 x 0 x 8.88 with resolution 25\n",
      "     block, center = (0.35,0,0)\n",
      "          size (0.7,0,2.3)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (4,4,4)\n",
      "time for set_epsilon = 0.053905 s\n",
      "-----------\n",
      "Meep: using complex fields.\n"
     ]
    },
    {
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       "model_id": "dd6ce618a7f24cafa0fee3b7c304ff6d",
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      },
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       "FloatProgress(value=0.0, description='0% done ', max=105.49778699874878)"
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     "output_type": "stream",
     "text": [
      "Meep progress: 35.94/105.49778699874878 = 34.1% done in 4.0s, 7.7s to go\n",
      "on time step 1803 (time=36.06), 0.00221964 s/step\n",
      "Meep progress: 72.2/105.49778699874878 = 68.4% done in 8.0s, 3.7s to go\n",
      "on time step 3616 (time=72.32), 0.00220671 s/step\n",
      "run 0 finished at t = 105.5 (5275 timesteps)\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import meep as mp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "r = 0.7  # radius of cylinder\n",
    "h = 2.3  # height of cylinder\n",
    "\n",
    "wvl_min = 2*np.pi*r/10\n",
    "wvl_max = 2*np.pi*r/2\n",
    "\n",
    "frq_min = 1/wvl_max\n",
    "frq_max = 1/wvl_min\n",
    "frq_cen = 0.5*(frq_min+frq_max)\n",
    "dfrq = frq_max-frq_min\n",
    "nfrq = 100\n",
    "\n",
    "## at least 8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min)\n",
    "resolution = 25\n",
    "\n",
    "dpml = 0.5*wvl_max\n",
    "dair = 1.0*wvl_max\n",
    "\n",
    "pml_layers = [mp.PML(thickness=dpml)]\n",
    "\n",
    "sr = r+dair+dpml\n",
    "sz = dpml+dair+h+dair+dpml\n",
    "cell_size = mp.Vector3(sr,0,sz)\n",
    "\n",
    "sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n",
    "                     component=mp.Er,\n",
    "                     center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n",
    "                     size=mp.Vector3(sr)),\n",
    "           mp.Source(mp.GaussianSource(frq_cen,fwidth=dfrq,is_integrated=True),\n",
    "                     component=mp.Ep,\n",
    "                     center=mp.Vector3(0.5*sr,0,-0.5*sz+dpml),\n",
    "                     size=mp.Vector3(sr),\n",
    "                     amplitude=-1j)]\n",
    "\n",
    "sim = mp.Simulation(cell_size=cell_size,\n",
    "                    boundary_layers=pml_layers,\n",
    "                    resolution=resolution,\n",
    "                    sources=sources,\n",
    "                    dimensions=mp.CYLINDRICAL,\n",
    "                    m=-1)\n",
    "\n",
    "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r)))\n",
    "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r)))\n",
    "box_r = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h)))\n",
    "\n",
    "sim.run(until_after_sources=10)\n",
    "\n",
    "freqs = mp.get_flux_freqs(box_z1)\n",
    "box_z1_data = sim.get_flux_data(box_z1)\n",
    "box_z2_data = sim.get_flux_data(box_z2)\n",
    "box_r_data = sim.get_flux_data(box_r)\n",
    "\n",
    "box_z1_flux0 = mp.get_fluxes(box_z1)\n",
    "\n",
    "sim.reset_meep()\n",
    "\n",
    "n_cyl = 2.0\n",
    "geometry = [mp.Block(material=mp.Medium(index=n_cyl),\n",
    "                     center=mp.Vector3(0.5*r),\n",
    "                     size=mp.Vector3(r,0,h))]\n",
    "\n",
    "sim = mp.Simulation(cell_size=cell_size,\n",
    "                    geometry=geometry,\n",
    "                    boundary_layers=pml_layers,\n",
    "                    resolution=resolution,\n",
    "                    sources=sources,\n",
    "                    dimensions=mp.CYLINDRICAL,\n",
    "                    m=-1)\n",
    "\n",
    "box_z1 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,-0.5*h),size=mp.Vector3(r)))\n",
    "box_z2 = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(0.5*r,0,+0.5*h),size=mp.Vector3(r)))\n",
    "box_r  = sim.add_flux(frq_cen, dfrq, nfrq, mp.FluxRegion(center=mp.Vector3(r),size=mp.Vector3(z=h)))\n",
    "\n",
    "sim.load_minus_flux_data(box_z1, box_z1_data)\n",
    "sim.load_minus_flux_data(box_z2, box_z2_data)\n",
    "sim.load_minus_flux_data(box_r, box_r_data)\n",
    "\n",
    "sim.run(until_after_sources=100)\n",
    "\n",
    "box_z1_flux = mp.get_fluxes(box_z1)\n",
    "box_z2_flux = mp.get_fluxes(box_z2)\n",
    "box_r_flux = mp.get_fluxes(box_r)\n",
    "\n",
    "scatt_flux = np.asarray(box_z1_flux)-np.asarray(box_z2_flux)-np.asarray(box_r_flux)\n",
    "intensity = np.asarray(box_z1_flux0)/(np.pi*r**2)\n",
    "scatt_cross_section = np.divide(-scatt_flux,intensity)\n",
    "\n",
    "plt.figure(dpi=150)\n",
    "plt.loglog(2*np.pi*r*np.asarray(freqs),scatt_cross_section,'bo-')\n",
    "plt.grid(True,which=\"both\",ls=\"-\")\n",
    "plt.xlabel('(cylinder circumference)/wavelength, 2πr/λ')\n",
    "plt.ylabel('scattering cross section, σ')\n",
    "plt.title('Scattering Cross Section of a Lossless Dielectric Cylinder')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the \"closed\" DFT flux box is comprised of just three flux objects: two along $z$ and one in the radial $r$ direction. The function `get_fluxes` which computes the integral of the Poynting vector is over the annular volume in cylindrical coordinates. There is no need for additional post-processing of the flux values.\n",
    "\n",
    "As shown below, the results for the scattering cross section computed using cylindrical coordinates agree well with the 3d Cartesian simulation. However, there is a large discrepancy in performance: for a single Intel Xeon 4.2GHz processor, the runtime of the cylindrical simulation is nearly 90 times shorter than the 3d simulation.\n",
    "\n",
    "![](https://meep.readthedocs.io/en/latest/images/cylinder_cross_section.png)"
   ]
  }
 ],
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